aigc-apps-EasyAnimate/easyanimate/data/dataset_image_video.py

import csv
import gc
import io
import json
import math
import os
import random
from contextlib import contextmanager
from threading import Thread

import albumentations
import cv2
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from decord import VideoReader
from einops import rearrange
from func_timeout import FunctionTimedOut, func_timeout
from packaging import version as pver
from PIL import Image
from torch.utils.data import BatchSampler, Sampler
from torch.utils.data.dataset import Dataset

VIDEO_READER_TIMEOUT = 20

def get_random_mask(shape):
    f, c, h, w = shape
    
    if f != 1:
        mask_index = np.random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], p=[0.05, 0.2, 0.2, 0.2, 0.05, 0.05, 0.05, 0.1, 0.05, 0.05]) 
    else:
        mask_index = np.random.choice([0, 1], p = [0.2, 0.8])
    mask = torch.zeros((f, 1, h, w), dtype=torch.uint8)

    if mask_index == 0:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围

        start_x = max(center_x - block_size_x // 2, 0)
        end_x = min(center_x + block_size_x // 2, w)
        start_y = max(center_y - block_size_y // 2, 0)
        end_y = min(center_y + block_size_y // 2, h)
        mask[:, :, start_y:end_y, start_x:end_x] = 1
    elif mask_index == 1:
        mask[:, :, :, :] = 1
    elif mask_index == 2:
        mask_frame_index = np.random.randint(1, 5)
        mask[mask_frame_index:, :, :, :] = 1
    elif mask_index == 3:
        mask_frame_index = np.random.randint(1, 5)
        mask[mask_frame_index:-mask_frame_index, :, :, :] = 1
    elif mask_index == 4:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        block_size_x = torch.randint(w // 4, w // 4 * 3, (1,)).item()  # 方块的宽度范围
        block_size_y = torch.randint(h // 4, h // 4 * 3, (1,)).item()  # 方块的高度范围

        start_x = max(center_x - block_size_x // 2, 0)
        end_x = min(center_x + block_size_x // 2, w)
        start_y = max(center_y - block_size_y // 2, 0)
        end_y = min(center_y + block_size_y // 2, h)

        mask_frame_before = np.random.randint(0, f // 2)
        mask_frame_after = np.random.randint(f // 2, f)
        mask[mask_frame_before:mask_frame_after, :, start_y:end_y, start_x:end_x] = 1
    elif mask_index == 5:
        mask = torch.randint(0, 2, (f, 1, h, w), dtype=torch.uint8)
    elif mask_index == 6:
        num_frames_to_mask = random.randint(1, max(f // 2, 1))
        frames_to_mask = random.sample(range(f), num_frames_to_mask)

        for i in frames_to_mask:
            block_height = random.randint(1, h // 4)
            block_width = random.randint(1, w // 4)
            top_left_y = random.randint(0, h - block_height)
            top_left_x = random.randint(0, w - block_width)
            mask[i, 0, top_left_y:top_left_y + block_height, top_left_x:top_left_x + block_width] = 1
    elif mask_index == 7:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        a = torch.randint(min(w, h) // 8, min(w, h) // 4, (1,)).item()  # 长半轴
        b = torch.randint(min(h, w) // 8, min(h, w) // 4, (1,)).item()  # 短半轴

        for i in range(h):
            for j in range(w):
                if ((i - center_y) ** 2) / (b ** 2) + ((j - center_x) ** 2) / (a ** 2) < 1:
                    mask[:, :, i, j] = 1
    elif mask_index == 8:
        center_x = torch.randint(0, w, (1,)).item()
        center_y = torch.randint(0, h, (1,)).item()
        radius = torch.randint(min(h, w) // 8, min(h, w) // 4, (1,)).item()
        for i in range(h):
            for j in range(w):
                if (i - center_y) ** 2 + (j - center_x) ** 2 < radius ** 2:
                    mask[:, :, i, j] = 1
    elif mask_index == 9:
        for idx in range(f):
            if np.random.rand() > 0.5:
                mask[idx, :, :, :] = 1
    else:
        raise ValueError(f"The mask_index {mask_index} is not define")
    return mask
 
class Camera(object):
    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
    """
    def __init__(self, entry):
        fx, fy, cx, cy = entry[1:5]
        self.fx = fx
        self.fy = fy
        self.cx = cx
        self.cy = cy
        w2c_mat = np.array(entry[7:]).reshape(3, 4)
        w2c_mat_4x4 = np.eye(4)
        w2c_mat_4x4[:3, :] = w2c_mat
        self.w2c_mat = w2c_mat_4x4
        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)

def custom_meshgrid(*args):
    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
    """
    # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
    if pver.parse(torch.__version__) < pver.parse('1.10'):
        return torch.meshgrid(*args)
    else:
        return torch.meshgrid(*args, indexing='ij')

def get_relative_pose(cam_params):
    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
    """
    abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
    abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
    cam_to_origin = 0
    target_cam_c2w = np.array([
        [1, 0, 0, 0],
        [0, 1, 0, -cam_to_origin],
        [0, 0, 1, 0],
        [0, 0, 0, 1]
    ])
    abs2rel = target_cam_c2w @ abs_w2cs[0]
    ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
    ret_poses = np.array(ret_poses, dtype=np.float32)
    return ret_poses

def ray_condition(K, c2w, H, W, device):
    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
    """
    # c2w: B, V, 4, 4
    # K: B, V, 4

    B = K.shape[0]

    j, i = custom_meshgrid(
        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
    )
    i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
    j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]

    fx, fy, cx, cy = K.chunk(4, dim=-1)  # B,V, 1

    zs = torch.ones_like(i)  # [B, HxW]
    xs = (i - cx) / fx * zs
    ys = (j - cy) / fy * zs
    zs = zs.expand_as(ys)

    directions = torch.stack((xs, ys, zs), dim=-1)  # B, V, HW, 3
    directions = directions / directions.norm(dim=-1, keepdim=True)  # B, V, HW, 3

    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)  # B, V, 3, HW
    rays_o = c2w[..., :3, 3]  # B, V, 3
    rays_o = rays_o[:, :, None].expand_as(rays_d)  # B, V, 3, HW
    # c2w @ dirctions
    rays_dxo = torch.cross(rays_o, rays_d)
    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)  # B, V, H, W, 6
    # plucker = plucker.permute(0, 1, 4, 2, 3)
    return plucker

def process_pose_file(pose_file_path, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
    """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
    """
    with open(pose_file_path, 'r') as f:
        poses = f.readlines()

    poses = [pose.strip().split(' ') for pose in poses[1:]]
    cam_params = [[float(x) for x in pose] for pose in poses]
    if return_poses:
        return cam_params
    else:
        cam_params = [Camera(cam_param) for cam_param in cam_params]

        sample_wh_ratio = width / height
        pose_wh_ratio = original_pose_width / original_pose_height  # Assuming placeholder ratios, change as needed

        if pose_wh_ratio > sample_wh_ratio:
            resized_ori_w = height * pose_wh_ratio
            for cam_param in cam_params:
                cam_param.fx = resized_ori_w * cam_param.fx / width
        else:
            resized_ori_h = width / pose_wh_ratio
            for cam_param in cam_params:
                cam_param.fy = resized_ori_h * cam_param.fy / height

        intrinsic = np.asarray([[cam_param.fx * width,
                                cam_param.fy * height,
                                cam_param.cx * width,
                                cam_param.cy * height]
                                for cam_param in cam_params], dtype=np.float32)

        K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
        c2ws = get_relative_pose(cam_params)  # Assuming this function is defined elsewhere
        c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
        plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous()  # V, 6, H, W
        plucker_embedding = plucker_embedding[None]
        plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
        return plucker_embedding

def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'):
    """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
    """
    cam_params = [Camera(cam_param) for cam_param in cam_params]

    sample_wh_ratio = width / height
    pose_wh_ratio = original_pose_width / original_pose_height  # Assuming placeholder ratios, change as needed

    if pose_wh_ratio > sample_wh_ratio:
        resized_ori_w = height * pose_wh_ratio
        for cam_param in cam_params:
            cam_param.fx = resized_ori_w * cam_param.fx / width
    else:
        resized_ori_h = width / pose_wh_ratio
        for cam_param in cam_params:
            cam_param.fy = resized_ori_h * cam_param.fy / height

    intrinsic = np.asarray([[cam_param.fx * width,
                            cam_param.fy * height,
                            cam_param.cx * width,
                            cam_param.cy * height]
                            for cam_param in cam_params], dtype=np.float32)

    K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
    c2ws = get_relative_pose(cam_params)  # Assuming this function is defined elsewhere
    c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
    plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous()  # V, 6, H, W
    plucker_embedding = plucker_embedding[None]
    plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
    return plucker_embedding

class ImageVideoSampler(BatchSampler):
    """A sampler wrapper for grouping images with similar aspect ratio into a same batch.

    Args:
        sampler (Sampler): Base sampler.
        dataset (Dataset): Dataset providing data information.
        batch_size (int): Size of mini-batch.
        drop_last (bool): If ``True``, the sampler will drop the last batch if
            its size would be less than ``batch_size``.
        aspect_ratios (dict): The predefined aspect ratios.
    """

    def __init__(self,
                 sampler: Sampler,
                 dataset: Dataset,
                 batch_size: int,
                 drop_last: bool = False
                ) -> None:
        if not isinstance(sampler, Sampler):
            raise TypeError('sampler should be an instance of ``Sampler``, '
                            f'but got {sampler}')
        if not isinstance(batch_size, int) or batch_size <= 0:
            raise ValueError('batch_size should be a positive integer value, '
                             f'but got batch_size={batch_size}')
        self.sampler = sampler
        self.dataset = dataset
        self.batch_size = batch_size
        self.drop_last = drop_last

        # buckets for each aspect ratio
        self.bucket = {'image':[], 'video':[]}

    def __iter__(self):
        for idx in self.sampler:
            content_type = self.dataset.dataset[idx].get('type', 'image')
            self.bucket[content_type].append(idx)

            # yield a batch of indices in the same aspect ratio group
            if len(self.bucket['video']) == self.batch_size:
                bucket = self.bucket['video']
                yield bucket[:]
                del bucket[:]
            elif len(self.bucket['image']) == self.batch_size:
                bucket = self.bucket['image']
                yield bucket[:]
                del bucket[:]

@contextmanager
def VideoReader_contextmanager(*args, **kwargs):
    vr = VideoReader(*args, **kwargs)
    try:
        yield vr
    finally:
        del vr
        gc.collect()

def get_video_reader_batch(video_reader, batch_index):
    frames = video_reader.get_batch(batch_index).asnumpy()
    return frames

def resize_frame(frame, target_short_side):
    h, w, _ = frame.shape
    if h < w:
        if target_short_side > h:
            return frame
        new_h = target_short_side
        new_w = int(target_short_side * w / h)
    else:
        if target_short_side > w:
            return frame
        new_w = target_short_side
        new_h = int(target_short_side * h / w)
    
    resized_frame = cv2.resize(frame, (new_w, new_h))
    return resized_frame

class ImageVideoDataset(Dataset):
    def __init__(
        self,
        ann_path, data_root=None,
        video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16,
        image_sample_size=512,
        video_repeat=0,
        text_drop_ratio=0.1,
        enable_bucket=False,
        video_length_drop_start=0.1, 
        video_length_drop_end=0.9,
        enable_inpaint=False,
    ):
        # Loading annotations from files
        print(f"loading annotations from {ann_path} ...")
        if ann_path.endswith('.csv'):
            with open(ann_path, 'r') as csvfile:
                dataset = list(csv.DictReader(csvfile))
        elif ann_path.endswith('.json'):
            dataset = json.load(open(ann_path))
    
        self.data_root = data_root

        # It's used to balance num of images and videos.
        self.dataset = []
        for data in dataset:
            if data.get('type', 'image') != 'video':
                self.dataset.append(data)
        if video_repeat > 0:
            for _ in range(video_repeat):
                for data in dataset:
                    if data.get('type', 'image') == 'video':
                        self.dataset.append(data)
        del dataset

        self.length = len(self.dataset)
        print(f"data scale: {self.length}")
        # TODO: enable bucket training
        self.enable_bucket = enable_bucket
        self.text_drop_ratio = text_drop_ratio
        self.enable_inpaint  = enable_inpaint

        self.video_length_drop_start = video_length_drop_start
        self.video_length_drop_end = video_length_drop_end

        # Video params
        self.video_sample_stride    = video_sample_stride
        self.video_sample_n_frames  = video_sample_n_frames
        self.video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
        self.video_transforms = transforms.Compose(
            [
                transforms.Resize(min(self.video_sample_size)),
                transforms.CenterCrop(self.video_sample_size),
                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
            ]
        )

        # Image params
        self.image_sample_size  = tuple(image_sample_size) if not isinstance(image_sample_size, int) else (image_sample_size, image_sample_size)
        self.image_transforms   = transforms.Compose([
            transforms.Resize(min(self.image_sample_size)),
            transforms.CenterCrop(self.image_sample_size),
            transforms.ToTensor(),
            transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
        ])

        self.larger_side_of_image_and_video = max(min(self.image_sample_size), min(self.video_sample_size))

    def get_batch(self, idx):
        data_info = self.dataset[idx % len(self.dataset)]
        
        if data_info.get('type', 'image')=='video':
            video_id, text = data_info['file_path'], data_info['text']

            if self.data_root is None:
                video_dir = video_id
            else:
                video_dir = os.path.join(self.data_root, video_id)

            with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
                min_sample_n_frames = min(
                    self.video_sample_n_frames, 
                    int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
                )
                if min_sample_n_frames == 0:
                    raise ValueError(f"No Frames in video.")

                video_length = int(self.video_length_drop_end * len(video_reader))
                clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
                start_idx   = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
                batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)

                try:
                    sample_args = (video_reader, batch_index)
                    pixel_values = func_timeout(
                        VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
                    )
                    resized_frames = []
                    for i in range(len(pixel_values)):
                        frame = pixel_values[i]
                        resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
                        resized_frames.append(resized_frame)
                    pixel_values = np.array(resized_frames)
                except FunctionTimedOut:
                    raise ValueError(f"Read {idx} timeout.")
                except Exception as e:
                    raise ValueError(f"Failed to extract frames from video. Error is {e}.")

                if not self.enable_bucket:
                    pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
                    pixel_values = pixel_values / 255.
                    del video_reader
                else:
                    pixel_values = pixel_values

                if not self.enable_bucket:
                    pixel_values = self.video_transforms(pixel_values)
                
                # Random use no text generation
                if random.random() < self.text_drop_ratio:
                    text = ''
            return pixel_values, text, 'video'
        else:
            image_path, text = data_info['file_path'], data_info['text']
            if self.data_root is not None:
                image_path = os.path.join(self.data_root, image_path)
            image = Image.open(image_path).convert('RGB')
            if not self.enable_bucket:
                image = self.image_transforms(image).unsqueeze(0)
            else:
                image = np.expand_dims(np.array(image), 0)
            if random.random() < self.text_drop_ratio:
                text = ''
            return image, text, 'image'

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        data_info = self.dataset[idx % len(self.dataset)]
        data_type = data_info.get('type', 'image')
        while True:
            sample = {}
            try:
                data_info_local = self.dataset[idx % len(self.dataset)]
                data_type_local = data_info_local.get('type', 'image')
                if data_type_local != data_type:
                    raise ValueError("data_type_local != data_type")

                pixel_values, name, data_type = self.get_batch(idx)
                sample["pixel_values"] = pixel_values
                sample["text"] = name
                sample["data_type"] = data_type
                sample["idx"] = idx
                
                if len(sample) > 0:
                    break
            except Exception as e:
                print(e, self.dataset[idx % len(self.dataset)])
                idx = random.randint(0, self.length-1)

        if self.enable_inpaint and not self.enable_bucket:
            mask = get_random_mask(pixel_values.size())
            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
            sample["mask_pixel_values"] = mask_pixel_values
            sample["mask"] = mask

            clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
            clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
            sample["clip_pixel_values"] = clip_pixel_values

            ref_pixel_values = sample["pixel_values"][0].unsqueeze(0)
            if (mask == 1).all():
                ref_pixel_values = torch.ones_like(ref_pixel_values) * -1
            sample["ref_pixel_values"] = ref_pixel_values

        return sample

class ImageVideoControlDataset(Dataset):
    def __init__(
        self,
        ann_path, data_root=None,
        video_sample_size=512, video_sample_stride=4, video_sample_n_frames=16,
        image_sample_size=512,
        video_repeat=0,
        text_drop_ratio=0.1,
        enable_bucket=False,
        video_length_drop_start=0.1, 
        video_length_drop_end=0.9,
        enable_inpaint=False,
        enable_camera_info=False,
    ):
        # Loading annotations from files
        print(f"loading annotations from {ann_path} ...")
        if ann_path.endswith('.csv'):
            with open(ann_path, 'r') as csvfile:
                dataset = list(csv.DictReader(csvfile))
        elif ann_path.endswith('.json'):
            dataset = json.load(open(ann_path))
    
        self.data_root = data_root

        # It's used to balance num of images and videos.
        self.dataset = []
        for data in dataset:
            if data.get('type', 'image') != 'video':
                self.dataset.append(data)
        if video_repeat > 0:
            for _ in range(video_repeat):
                for data in dataset:
                    if data.get('type', 'image') == 'video':
                        self.dataset.append(data)
        del dataset

        self.length = len(self.dataset)
        print(f"data scale: {self.length}")
        # TODO: enable bucket training
        self.enable_bucket = enable_bucket
        self.text_drop_ratio = text_drop_ratio
        self.enable_inpaint  = enable_inpaint
        self.enable_camera_info = enable_camera_info

        self.video_length_drop_start = video_length_drop_start
        self.video_length_drop_end = video_length_drop_end

        # Video params
        self.video_sample_stride    = video_sample_stride
        self.video_sample_n_frames  = video_sample_n_frames
        self.video_sample_size = tuple(video_sample_size) if not isinstance(video_sample_size, int) else (video_sample_size, video_sample_size)
        self.video_transforms = transforms.Compose(
            [
                transforms.Resize(min(self.video_sample_size)),
                transforms.CenterCrop(self.video_sample_size),
                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
            ]
        )
        if self.enable_camera_info:
            self.video_transforms_camera = transforms.Compose(
                [
                    transforms.Resize(min(self.video_sample_size)),
                    transforms.CenterCrop(self.video_sample_size)
                ]
            )

        # Image params
        self.image_sample_size  = tuple(image_sample_size) if not isinstance(image_sample_size, int) else (image_sample_size, image_sample_size)
        self.image_transforms   = transforms.Compose([
            transforms.Resize(min(self.image_sample_size)),
            transforms.CenterCrop(self.image_sample_size),
            transforms.ToTensor(),
            transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])
        ])

        self.larger_side_of_image_and_video = max(min(self.image_sample_size), min(self.video_sample_size))
    
    def get_batch(self, idx):
        data_info = self.dataset[idx % len(self.dataset)]
        video_id, text = data_info['file_path'], data_info['text']

        if data_info.get('type', 'image')=='video':
            if self.data_root is None:
                video_dir = video_id
            else:
                video_dir = os.path.join(self.data_root, video_id)

            with VideoReader_contextmanager(video_dir, num_threads=2) as video_reader:
                min_sample_n_frames = min(
                    self.video_sample_n_frames, 
                    int(len(video_reader) * (self.video_length_drop_end - self.video_length_drop_start) // self.video_sample_stride)
                )
                if min_sample_n_frames == 0:
                    raise ValueError(f"No Frames in video.")

                video_length = int(self.video_length_drop_end * len(video_reader))
                clip_length = min(video_length, (min_sample_n_frames - 1) * self.video_sample_stride + 1)
                start_idx   = random.randint(int(self.video_length_drop_start * video_length), video_length - clip_length) if video_length != clip_length else 0
                batch_index = np.linspace(start_idx, start_idx + clip_length - 1, min_sample_n_frames, dtype=int)

                try:
                    sample_args = (video_reader, batch_index)
                    pixel_values = func_timeout(
                        VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
                    )
                    resized_frames = []
                    for i in range(len(pixel_values)):
                        frame = pixel_values[i]
                        resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
                        resized_frames.append(resized_frame)
                    pixel_values = np.array(resized_frames)
                except FunctionTimedOut:
                    raise ValueError(f"Read {idx} timeout.")
                except Exception as e:
                    raise ValueError(f"Failed to extract frames from video. Error is {e}.")

                if not self.enable_bucket:
                    pixel_values = torch.from_numpy(pixel_values).permute(0, 3, 1, 2).contiguous()
                    pixel_values = pixel_values / 255.
                    del video_reader
                else:
                    pixel_values = pixel_values

                if not self.enable_bucket:
                    pixel_values = self.video_transforms(pixel_values)
                
                # Random use no text generation
                if random.random() < self.text_drop_ratio:
                    text = ''

            control_video_id = data_info['control_file_path']

            if self.data_root is None:
                control_video_id = control_video_id
            else:
                control_video_id = os.path.join(self.data_root, control_video_id)
            
            if self.enable_camera_info:
                if control_video_id.lower().endswith('.txt'):
                    if not self.enable_bucket:
                        control_pixel_values = torch.zeros_like(pixel_values)

                        control_camera_values = process_pose_file(control_video_id, width=self.video_sample_size[1], height=self.video_sample_size[0])
                        control_camera_values = torch.from_numpy(control_camera_values).permute(0, 3, 1, 2).contiguous()
                        control_camera_values = F.interpolate(control_camera_values, size=(len(video_reader), control_camera_values.size(3)), mode='bilinear', align_corners=True)
                        control_camera_values = self.video_transforms_camera(control_camera_values)
                    else:
                        control_pixel_values = np.zeros_like(pixel_values)

                        control_camera_values = process_pose_file(control_video_id, width=self.video_sample_size[1], height=self.video_sample_size[0], return_poses=True)
                        control_camera_values = torch.from_numpy(np.array(control_camera_values)).unsqueeze(0).unsqueeze(0)
                        control_camera_values = F.interpolate(control_camera_values, size=(len(video_reader), control_camera_values.size(3)), mode='bilinear', align_corners=True)[0][0]
                        control_camera_values = np.array([control_camera_values[index] for index in batch_index])
                else:
                    if not self.enable_bucket:
                        control_pixel_values = torch.zeros_like(pixel_values)
                        control_camera_values = None
                    else:
                        control_pixel_values = np.zeros_like(pixel_values)
                        control_camera_values = None
            else:
                with VideoReader_contextmanager(control_video_id, num_threads=2) as control_video_reader:
                    try:
                        sample_args = (control_video_reader, batch_index)
                        control_pixel_values = func_timeout(
                            VIDEO_READER_TIMEOUT, get_video_reader_batch, args=sample_args
                        )
                        resized_frames = []
                        for i in range(len(control_pixel_values)):
                            frame = control_pixel_values[i]
                            resized_frame = resize_frame(frame, self.larger_side_of_image_and_video)
                            resized_frames.append(resized_frame)
                        control_pixel_values = np.array(resized_frames)
                    except FunctionTimedOut:
                        raise ValueError(f"Read {idx} timeout.")
                    except Exception as e:
                        raise ValueError(f"Failed to extract frames from video. Error is {e}.")

                    if not self.enable_bucket:
                        control_pixel_values = torch.from_numpy(control_pixel_values).permute(0, 3, 1, 2).contiguous()
                        control_pixel_values = control_pixel_values / 255.
                        del control_video_reader
                    else:
                        control_pixel_values = control_pixel_values

                    if not self.enable_bucket:
                        control_pixel_values = self.video_transforms(control_pixel_values)
                control_camera_values = None

            return pixel_values, control_pixel_values, control_camera_values, text, "video"
        else:
            image_path, text = data_info['file_path'], data_info['text']
            if self.data_root is not None:
                image_path = os.path.join(self.data_root, image_path)
            image = Image.open(image_path).convert('RGB')
            if not self.enable_bucket:
                image = self.image_transforms(image).unsqueeze(0)
            else:
                image = np.expand_dims(np.array(image), 0)

            if random.random() < self.text_drop_ratio:
                text = ''

            control_image_id = data_info['control_file_path']

            if self.data_root is None:
                control_image_id = control_image_id
            else:
                control_image_id = os.path.join(self.data_root, control_image_id)

            control_image = Image.open(control_image_id).convert('RGB')
            if not self.enable_bucket:
                control_image = self.image_transforms(control_image).unsqueeze(0)
            else:
                control_image = np.expand_dims(np.array(control_image), 0)

            return image, control_image, None, text, 'image'

    def __len__(self):
        return self.length

    def __getitem__(self, idx):
        data_info = self.dataset[idx % len(self.dataset)]
        data_type = data_info.get('type', 'image')
        while True:
            sample = {}
            try:
                data_info_local = self.dataset[idx % len(self.dataset)]
                data_type_local = data_info_local.get('type', 'image')
                if data_type_local != data_type:
                    raise ValueError("data_type_local != data_type")

                pixel_values, control_pixel_values, control_camera_values, name, data_type = self.get_batch(idx)

                sample["pixel_values"] = pixel_values
                sample["control_pixel_values"] = control_pixel_values
                sample["text"] = name
                sample["data_type"] = data_type
                sample["idx"] = idx

                if self.enable_camera_info:
                    sample["control_camera_values"] = control_camera_values

                if len(sample) > 0:
                    break
            except Exception as e:
                print(e, self.dataset[idx % len(self.dataset)])
                idx = random.randint(0, self.length-1)

        if self.enable_inpaint and not self.enable_bucket:
            mask = get_random_mask(pixel_values.size())
            mask_pixel_values = pixel_values * (1 - mask) + torch.ones_like(pixel_values) * -1 * mask
            sample["mask_pixel_values"] = mask_pixel_values
            sample["mask"] = mask

            clip_pixel_values = sample["pixel_values"][0].permute(1, 2, 0).contiguous()
            clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
            sample["clip_pixel_values"] = clip_pixel_values

            ref_pixel_values = sample["pixel_values"][0].unsqueeze(0)
            if (mask == 1).all():
                ref_pixel_values = torch.ones_like(ref_pixel_values) * -1
            sample["ref_pixel_values"] = ref_pixel_values

        return sample

if __name__ == "__main__":
    dataset = ImageVideoDataset(
        ann_path="test.json"
    )
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=4, num_workers=16)
    for idx, batch in enumerate(dataloader):
        print(batch["pixel_values"].shape, len(batch["text"]))